notes on regression and time series analysis

This web site
contains notes and materials for an advanced elective course on statistical
forecasting that is taught at the Fuqua School of Business, Duke University. It
covers linear regression and time series forecasting models as well as general
principles of thoughtful data analysis. The time series material is illustrated
with output produced by Statgraphics,
a statistical software package that is highly interactive and has good features
for testing and comparing models, including a parallel-model forecasting
procedure that I designed many years ago. The material on multivariate data
analysis and linear regression is illustrated with output produced by RegressIt, a free* *Excel add-in developed more recently which offers
presentation-quality graphics and support for good modeling practices. However,
these notes are platform-independent. Any statistical software package ought to
provide the analytical capabilities needed for the various topics covered here.

Principles and risks
of forecasting (pdf)

Famous forecasting quotes

How to move data around

Get to know your data

Inflation adjustment (deflation)

Seasonal adjustment

Stationarity and differencing

The logarithm transformation

Statistics
review and the simplest forecasting model: the sample mean (pdf)

Notes on the random
walk model (pdf)

Mean (constant) model

Linear trend model

Random walk model

Geometric random walk model

Three types of forecasts: estimation period, validation
period, and the future

Notes on
forecasting with moving averages (pdf)

Moving average and exponential smoothing models

Slides
on inflation and seasonal adjustment and Winters seasonal exponential smoothing

Spreadsheet implementation of seasonal adjustment and
exponential smoothing

Equations
for the smoothing models (SAS web site)

Notes on linear
regression analysis (pdf)

Introduction to linear regression analysis

Mathematics
of simple regression

Regression examples

- Beer sales
vs. price, part 1: descriptive analysis

- Beer sales
vs. price, part 2: fitting a simple model

- Beer sales
vs. price, part 3: transformations of variables

- Beer sales
vs. price, part 4: additional predictors

What to look for in regression output

What's a good value for R-squared?

What's the bottom line? How to compare models

Testing the assumptions of linear regression

Additional notes on regression analysis

Spreadsheet with
regression formulas

Stepwise and all-possible-regressions

RegressIt: free Excel add-in for linear regression
and multivariate data analysis

Notes on nonseasonal
ARIMA models (pdf)

Slides on seasonal and
nonseasonal ARIMA models (pdf)

Introduction to ARIMA: nonseasonal models

Identifying the order of differencing

Identifying the orders of AR or MA terms

Estimation of ARIMA models

Seasonal differencing

Seasonal random walk: ARIMA(0,0,0)x(0,1,0)

Seasonal random trend: ARIMA(0,1,0)x(0,1,0)

General seasonal ARIMA models: ARIMA(0,1,1)x(0,1,1) etc.

Summary of rules for identifying ARIMA models

ARIMA models with regressors

The
mathematical structure of ARIMA models (pdf)

Steps in choosing a forecasting model

Forecasting flow chart

Data transformations and forecasting models: what to use
and when

Automatic forecasting software

Political and ethical issues in forecasting

How to avoid trouble: principles of good data analysis

International
Institute of Forecasters links (sites, references, software)

Forecasting Principles and Practice
on-line textbook (Rob Hyndman and George Athanasopoulos)

Forecasting Principles web site
(J. Scott Armstrong and Kesten Green)

Online Statistics: Interactive
Multimedia Course (David Lane)

HyperStat Online web site (David
Lane)

StatPages web site (John Pezzullo)

StatSci web site (Gordon Smyth)

Statistics.com web site (online education)

Talk Stats forum

StackExchange Cross-Validated forum

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in the past. Last updated on December 11, 2014.